Yesterday, DailyTech ran a story about details on Intel's upcoming Larrabee architecture for the
graphics market. One of Intel's most important talking points when it plays up
the benefits of Larrabee over
NVIDIA's GPUs is the fact that NVIDIA's GPUs require developers to learn a new
programming language called CUDA.

Intel says that with its Larrabee architecture developers can
simply program in C or C++ languages for just as they would for any other x86
processor. According to Intel, the ability to program Larrabee with C or C++ makes it much easier for developers to port
applications from other platforms to the Larrabee
architecture.

After DailyTech ran the story, NVIDIA
wanted to address what it considers to be misinformation when it comes to CUDA.
NVIDIA says:

CUDA
is a C-language compiler that is based on the PathScale C compiler. This open
source compiler was originally developed for the x86 architecture. The NVIDIA
computing architecture was specifically designed to support the C language -
like any other processor architecture. Competitive comments that the GPU is
only partially programmable are incorrect - all the processors in the NVIDIA
GPU are programmable in the C language.

NVIDIA's approach to parallel computing has already proven
to scale from 8 to 240 GPU cores. Also, NVIDIA is just about to release a
multi-core CPU version of the CUDA compiler. This allows the developer to write
an application once and run across multiple platforms. Larrabee's development
environment is proprietary to Intel and, at least disclosed in marketing
materials to date, is different than a multi-core CPU software environment.

Andrew Humber from NVIDIA distilled
things a bit further saying, "CUDA is just our brand name for the
C-compiler. They aren't two different things."

Humber also pointed out that at
NVIDIA's financial analyst day in April it showed an astrophysics simulation
running on integrated graphics with an eight-core GPU, a GeForce 8 series GPU
with 128 cores and a quad-core CPU. NVIDIA says that the
demonstration used exactly the same binary program across the range of GPUs and
the exact same source code for the CPU and GPU.

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